基于ROS系统的作物图像自主采集系统

doi:10.12398/j.issn.2096-7217.2023.02.006

智能化农业装备学报（中英文） ›› 2023, Vol. 4 ›› Issue (2): 53-62.DOI: 10.12398/j.issn.2096-7217.2023.02.006

基于ROS系统的作物图像自主采集系统

李易达^{1, 2}，汪刘洋^{1, 3}，韩雨晓^{1, 3}，李帅^{1, 2}，李寒^{1, 3*}，张漫^{1, 2}

1. 中国农业大学，北京市，100083; 2. 智慧农业系统集成研究教育部重点实验室，北京市，100083；
3. 农业农村部农业信息获取技术重点实验室，北京市，100083

出版日期:2023-05-15 发布日期:2023-05-15
通讯作者: 李寒，女，1986年生，山东聊城人，博士，副教授；研究方向为农业电气化与自动化。E-mail: cau_lihan@cau.edu.cn
作者简介:李易达，男，2000年生，河北石家庄人，硕士研究生；研究方向为马铃薯水分胁迫研究。E-mail: s20223081705@cau.edu.cn
基金资助:
国家自然科学基金项目(32171893)

Crop image autonomous acquisition system based on ROS system

LI Yida^{1, 2}, WANG Liuyang^{1, 3}, HAN Yuxiao^{1, 3}, LI Shuai^{1, 2}, LI Han^{1, 3*}， ZHANG Man^{1, 2}#br#

1. China Agricultural University, Beijing 100083, China; 2. Key Laboratory of Smart Agriculture System Integration, Ministry of Education, Beijing 100083, China; 3. Key Laboratory of Agricultural Information Acquisition Technology，Ministry of Agriculture and Rural Affairs, Beijing 100083, China

Online:2023-05-15 Published:2023-05-15

摘要/Abstract

摘要： 马铃薯水分胁迫状态监测方法研究对于马铃薯生长品质和产量提升有重要作用。基于热红外图像和可见光图像可以计算得到作物水分胁迫指数（crop water stress index，CWSI），并进行作物水分胁迫状态的判断。为了自动化、高通量、无损的连续采集可见光、热红外图像和原始温度数据，本研究集成开发了双目相机和热红外相机的小型作物巡检图像采集系统。根据不同功能需求，对硬件部分进行分类模块化设计，包括主控模块、运动控制模块、图像采集模块和实时监控模块，其中主控模块实现对图像采集模块的拍照控制、对图像和温度数据的传输保存、与运动控制模块数据交互。基于机器人操作系统(ROS)架构设计多个节点以发布/订阅的模式实现各个模块节点之间通信，包括运动控制节点、热红外相机节点和双目相机节点。通过实验室内1d的系统可行性测试得出，运动模块控制精度满足图像采集需求，各个模块和节点控制程序可以协同完成图像采集和本地存储，通过在温室内为期18d系统稳定性测试与应用，共进行648次巡检采集，每次巡检耗时3 min，得到11 664张可见光图像，5 832张热红外图像，5 832份原始温度数据，证明系统运行稳定、图像、温度数据正常采集，实现自动化、高通量、无损获取所需数据的功能。该系统为作物表型信息获取提供了一种有效的技术和装备支撑。

关键词: 图像采集, 作物表型, 水分胁迫, 机器人操作系统, 马铃薯

Abstract: The research on monitoring methods of potato water stress status plays an important role in improving potato growth quality and yield. Based on thermal infrared and visible RGB images, the Crop Water Stress Index (CWSI) can be calculated and used to determine the status of crop water stress. To achieve automated, high-throughput, and non-destructive continuous collection of visible RGB, thermal infrared images, and raw temperature data, this study integrated and developed a crop inspection image acquisition system with binocular cameras and thermal infrared cameras. According to different functional requirements, the hardware parts were designed as different modules, including the main control module, motion control module, image acquisition module, and real-time monitoring module. The main control module implements photo taking control of the image acquisition module, transmission and storage of image and temperature data, and data interaction with the motion control module. Based on the robot operating system (ROS) architecture, multiple nodes were designed to communicate between each module node in publish/subscribe mode, including motion control node, thermal infrared camera node and binocular camera node. Through the one-day system feasibility test in the laboratory, it was concluded that the control accuracy of the motion module meets the needs of image acquisition. Each module and node control program can cooperate to complete image acquisition and local storage. Through the 18-day system stability test and application in the greenhouse, a total of 648 inspections were carried out, each inspection took 3 minutes, and 11 664 visible light images, 5 832 thermal infrared images and 5 832 original temperature data were obtained. It was proved that the system was running stably, the image and temperature data were collected normally, and the function of automatic, high-throughput and non-destructive acquisition of the required data have been realized. The system provides an effective technical and equipment support for crop phenotypic information acquisition.

Key words: image acquisition, crop phenotype, water stress, robot operating system, potatoes

中图分类号:

李易达, 汪刘洋, 韩雨晓, 李帅, 李寒, 张漫. 基于ROS系统的作物图像自主采集系统[J]. 智能化农业装备学报（中英文）, 2023, 4(2): 53-62.

LI Yida, WANG Liuyang, HAN Yuxiao, LI Shuai, LI Han, ZHANG Man. Crop image autonomous acquisition system based on ROS system[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(2): 53-62.

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基于ROS系统的作物图像自主采集系统

Crop image autonomous acquisition system based on ROS system

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